Fast optimization solution and road tests for predictive cruise control of heavy-duty trucks

To fully exploit the truck’s energy-saving potential, optimization-based methods are typically used to control the powertrain system, in which Predictive Cruise Control (PCC) is an effective method for improving energy efficiency. Due to nonlinear characteristics and long prediction horizon, the optimization problem becomes too complex to solve, making it difficult to meet the real-time computing requirements of embedded on-board platforms. This paper proposes a fast solution algorithm for PCC based on Pontryagin’s Maximum Principle (PMP), where warm-start mechanism is applied to accelerate the convergence speed of the solution searching bisection method, resulting in a 70.83% improvement in calculation speed compared to the standard bisection method. Finally, the proposed algorithm is tested on commercially heavy-duty trucks. Experimental results show that in high-speed conditions, fuel economy is improved by 4.66% over the rule-based PCC system. Additionally, the algorithm demonstrates improvements of over 5% in driving comfort and over 6% in speed tracking performance.